R Notebook
library(groundhog)## Loaded 'groundhog' (version:2.0.1) using R-4.2.1## Tips and troubleshooting: https://groundhogR.com## [1;36mgroundhog says:[0m## [36m
##
##
## OUTDATED GROUNDHOG
## You are using version '2.0.1
## The current version is '2.1.0'
##
## You can read about the changes here: https://groundhogr.com/changelog
##
## Update by running:
## install.packages('groundhog')[0mpkgs <- c("tidyverse","here", "lmerTest", "sjPlot","broom.mixed", "kableExtra", "ggeffects", "gt", "brms", "bayestestR","ggdist", "pheatmap", "heatmaply","pheatmap","gplots","RColorBrewer", "tm", "wordcloud", "psych")
groundhog.day <- '2022-07-25'
groundhog.library(pkgs, groundhog.day)## Registered S3 methods overwritten by 'readr':
## method from
## as.data.frame.spec_tbl_df vroom
## as_tibble.spec_tbl_df vroom
## format.col_spec vroom
## print.col_spec vroom
## print.collector vroom
## print.date_names vroom
## print.locale vroom
## str.col_spec vroom## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## [36mSuccesfully attached 'tidyverse_1.3.2'[0m
##
## here() starts at /Users/jacobelder/Documents/GitHub/EpMemNet
##
## [36mSuccesfully attached 'here_1.0.1'[0m
##
## Loading required package: lme4
##
## Loading required package: Matrix
##
##
## Attaching package: 'Matrix'
##
##
## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack
##
##
##
## Attaching package: 'lmerTest'
##
##
## The following object is masked from 'package:lme4':
##
## lmer
##
##
## The following object is masked from 'package:stats':
##
## step
##
##
## [36mSuccesfully attached 'lmerTest_3.1-3'[0m
##
## [36mSuccesfully attached 'sjPlot_2.8.10'[0m
##
## [36mSuccesfully attached 'broom.mixed_0.2.9.4'[0m
##
##
## Attaching package: 'kableExtra'
##
##
## The following object is masked from 'package:dplyr':
##
## group_rows
##
##
## [36mSuccesfully attached 'kableExtra_1.3.4'[0m
##
## [36mSuccesfully attached 'ggeffects_1.1.2'[0m
##
## [36mSuccesfully attached 'gt_0.6.0'[0m
##
## Loading required package: Rcpp
##
## Loading 'brms' package (version 2.17.0). Useful instructions
## can be found by typing help('brms'). A more detailed introduction
## to the package is available through vignette('brms_overview').
##
##
## Attaching package: 'brms'
##
##
## The following object is masked from 'package:lme4':
##
## ngrps
##
##
## The following object is masked from 'package:stats':
##
## ar
##
##
## [36mSuccesfully attached 'brms_2.17.0'[0m
##
## [36mSuccesfully attached 'bayestestR_0.12.1'[0m
##
##
## Attaching package: 'ggdist'
##
##
## The following object is masked from 'package:bayestestR':
##
## hdi
##
##
## The following objects are masked from 'package:brms':
##
## dstudent_t, pstudent_t, qstudent_t, rstudent_t
##
##
## [36mSuccesfully attached 'ggdist_3.2.0'[0m
##
## [36mSuccesfully attached 'pheatmap_1.0.12'[0m
##
## Registered S3 method overwritten by 'seriation':
## method from
## reorder.hclust gclus
##
## Loading required package: plotly
##
##
## Attaching package: 'plotly'
##
##
## The following object is masked from 'package:ggplot2':
##
## last_plot
##
##
## The following object is masked from 'package:stats':
##
## filter
##
##
## The following object is masked from 'package:graphics':
##
## layout
##
##
## Loading required package: viridis
##
## Loading required package: viridisLite
##
##
## ======================
## Welcome to heatmaply version 1.3.0
##
## Type citation('heatmaply') for how to cite the package.
## Type ?heatmaply for the main documentation.
##
## The github page is: https://github.com/talgalili/heatmaply/
## Please submit your suggestions and bug-reports at: https://github.com/talgalili/heatmaply/issues
## You may ask questions at stackoverflow, use the r and heatmaply tags:
## https://stackoverflow.com/questions/tagged/heatmaply
## ======================
##
##
## [36mSuccesfully attached 'heatmaply_1.3.0'[0m
##
## [36mThe package 'pheatmap_1.0.12' is already attached.[0m
##
##
## Attaching package: 'gplots'
##
##
## The following object is masked from 'package:stats':
##
## lowess
##
##
## [36mSuccesfully attached 'gplots_3.1.3'[0m
##
## [36mSuccesfully attached 'RColorBrewer_1.1-3'[0m
##
## Loading required package: NLP
##
##
## Attaching package: 'NLP'
##
##
## The following object is masked from 'package:ggplot2':
##
## annotate
##
##
## [36mSuccesfully attached 'tm_0.7-8'[0m
##
##
## Attaching package: 'wordcloud'
##
##
## The following object is masked from 'package:gplots':
##
## textplot
##
##
## [36mSuccesfully attached 'wordcloud_2.6'[0m
##
##
## Attaching package: 'psych'
##
##
## The following object is masked from 'package:brms':
##
## cs
##
##
## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha
##
##
## [36mSuccesfully attached 'psych_2.2.5'[0mhere::i_am("Analysis/idmPrelimAnal.Rmd")## here() starts at /Users/jacobelder/Documents/GitHub/EpMemNetdevtools::source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/corToOne.R")## ℹ SHA-1 hash of file is "07e3c11d2838efe15b1a6baf5ba2694da3f28cb1"devtools::source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/plotCommAxes.R")## ℹ SHA-1 hash of file is "374a4de7fec345d21628a52c0ed0e4f2c389df8e"devtools::source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/named.effects.ref.R")## ℹ SHA-1 hash of file is "611f7a9f62fa079ff4018b7aaef881791942546d"fullLong <- arrow::read_parquet(here("Data", "longEpMNet.parquet"))
fullShort <- arrow::read_parquet(here("Data","shortEpMNet.parquet"))
fullLong$subID <- as.numeric(fullLong$subID)
fullData <- fullLong %>% full_join(fullShort, by = c("subID"))Descriptives
# How many people listed 0 connections?
nrow(fullShort[which(fullShort$edgeTot==0),])## [1] 19#describe(fullLong$strength)Wordcloud
#Create a vector containing only the text
text <- as.vector(fullData$memory)
# Create a corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)## Warning in tm_map.SimpleCorpus(., removeNumbers): transformation drops documents## Warning in tm_map.SimpleCorpus(., removePunctuation): transformation drops
## documents## Warning in tm_map.SimpleCorpus(., stripWhitespace): transformation drops
## documentsdocs <- tm_map(docs, content_transformer(tolower))## Warning in tm_map.SimpleCorpus(docs, content_transformer(tolower)):
## transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))## Warning in tm_map.SimpleCorpus(docs, removeWords, stopwords("english")):
## transformation drops documentsdocs <- tm_map(docs, removeWords, c("the","and"))## Warning in tm_map.SimpleCorpus(docs, removeWords, c("the", "and")):
## transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df <- data.frame(word = names(words),freq=words)
set.seed(24)
wordcloud(words = df$word, freq = df$freq, min.freq = 1, max.words=200, random.order=FALSE, rot.per=0.35, colors=brewer.pal(8, "Dark2"))
Sanity Checks
Does time predict number of causes?
Yes, the farther away in time, the more experiences something causes.
m<-glmer(outdegree ~ scale(length) + numID + ( scale(length) | subID), data=fullData,family="poisson")
summary(m)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: outdegree ~ scale(length) + numID + (scale(length) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7829.3 7863.5 -3908.7 7817.3 2209
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3004 -0.9114 -0.1534 0.5892 11.2174
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.23383 0.4836
## scale(length) 0.08941 0.2990 -0.13
## Number of obs: 2215, groups: subID, 215
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.283669 0.077167 -3.676 0.000237 ***
## scale(length) 0.114670 0.035697 3.212 0.001317 **
## numID 0.038627 0.004514 8.557 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(l)
## scal(lngth) -0.088
## numID -0.816 0.054Does time predict causes of experience?
Yes, the farther back in time, the fewer experiences cause something.
m<-glmer(indegree ~ scale(length) + numID + ( scale(length) | subID), data=fullData,family="poisson")
summary(m)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: indegree ~ scale(length) + numID + (scale(length) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7458.3 7492.5 -3723.1 7446.3 2209
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9442 -0.6458 -0.1297 0.2974 9.5454
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.26312 0.5130
## scale(length) 0.07689 0.2773 -0.25
## Number of obs: 2215, groups: subID, 215
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.185839 0.078373 -2.371 0.0177 *
## scale(length) -0.147931 0.037409 -3.954 7.67e-05 ***
## numID 0.033445 0.004683 7.142 9.20e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(l)
## scal(lngth) 0.004
## numID -0.809 -0.027H1: People will evaluate more positively, less negatively (i.e., more favorably) on memories with more downstram dependents.
Valence Self-Report
More positive and negative, experience causes more experience
More positive, experience is caused by more experiences
m<-glmer(outdegree ~ scale(positive) + scale(negative) + numID + ( scale(positive) + scale(negative) | subID), data=fullData,family="poisson")
summary(m)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula:
## outdegree ~ scale(positive) + scale(negative) + numID + (scale(positive) +
## scale(negative) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 5185.0 5237.5 -2582.5 5165.0 1394
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1054 -0.9130 -0.1103 0.6085 7.7908
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.16785 0.4097
## scale(positive) 0.05166 0.2273 -0.07
## scale(negative) 0.04591 0.2143 -0.20 0.83
## Number of obs: 1404, groups: subID, 204
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.096675 0.076445 -1.265 0.20600
## scale(positive) 0.136995 0.044201 3.099 0.00194 **
## scale(negative) 0.152951 0.046860 3.264 0.00110 **
## numID 0.039450 0.004358 9.053 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(p) scl(n)
## scale(pstv) 0.151
## scale(ngtv) 0.137 0.771
## numID -0.816 -0.026 -0.040m<-glmer(indegree ~ scale(positive) + scale(negative) + numID + ( scale(positive) + scale(negative) | subID), data=fullData,family="poisson")## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0114173 (tol = 0.002, component 1)summary(m)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula:
## indegree ~ scale(positive) + scale(negative) + numID + (scale(positive) +
## scale(negative) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 5028.9 5081.4 -2504.5 5008.9 1394
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3588 -0.6642 -0.1111 0.3082 8.6045
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.238347 0.48821
## scale(positive) 0.006408 0.08005 0.22
## scale(negative) 0.067935 0.26064 0.23 1.00
## Number of obs: 1404, groups: subID, 204
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.121492 0.081602 -1.489 0.136533
## scale(positive) 0.138182 0.039441 3.504 0.000459 ***
## scale(negative) 0.056178 0.051405 1.093 0.274466
## numID 0.035139 0.004771 7.364 1.78e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(p) scl(n)
## scale(pstv) 0.145
## scale(ngtv) 0.182 0.732
## numID -0.788 0.056 0.038
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0114173 (tol = 0.002, component 1)m<-lmer(positive ~ outdegree + indegree + numID + ( outdegree + indegree | subID), data=fullData)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: positive ~ outdegree + indegree + numID + (outdegree + indegree |
## subID)
## Data: fullData
##
## REML criterion at convergence: 18148.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0855 -0.4876 0.2808 0.6541 2.0780
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 254.519 15.954
## outdegree 3.762 1.940 -0.44
## indegree 2.425 1.557 -0.61 0.04
## Residual 809.399 28.450
## Number of obs: 1879, groups: subID, 210
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 71.6835 2.1169 304.3606 33.863 <2e-16 ***
## outdegree -0.1219 0.4417 62.4226 -0.276 0.7834
## indegree 1.1853 0.4287 27.0244 2.765 0.0101 *
## numID -0.1147 0.1329 177.1610 -0.863 0.3891
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) outdgr indegr
## outdegree -0.166
## indegree -0.145 -0.104
## numID -0.654 -0.208 -0.265m<-lmer(negative ~ outdegree + indegree + numID + ( outdegree + indegree | subID), data=fullData)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: negative ~ outdegree + indegree + numID + (outdegree + indegree |
## subID)
## Data: fullData
##
## REML criterion at convergence: 15342.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8807 -0.7812 -0.1741 0.7629 2.5569
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 308.115 17.553
## outdegree 1.812 1.346 -0.20
## indegree 6.301 2.510 -0.63 0.41
## Residual 1032.610 32.134
## Number of obs: 1548, groups: subID, 207
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 40.5571 2.5658 265.9755 15.807 <2e-16 ***
## outdegree 0.2995 0.4915 36.2620 0.609 0.5461
## indegree -1.0552 0.5875 37.7387 -1.796 0.0805 .
## numID 0.1652 0.1586 163.0605 1.042 0.2992
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) outdgr indegr
## outdegree -0.182
## indegree -0.202 0.008
## numID -0.664 -0.163 -0.238PANAS
Outdegree
More positive and more negative, experience causes more experiences
m<-glmer(outdegree ~ scale(PANAS_P) + scale(PANAS_N) + numID + ( scale(PANAS_P) + scale(PANAS_N) | subID), data=fullData,family="poisson")## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0420052 (tol = 0.002, component 1)summary(m)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula:
## outdegree ~ scale(PANAS_P) + scale(PANAS_N) + numID + (scale(PANAS_P) +
## scale(PANAS_N) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7289.2 7345.3 -3634.6 7269.2 2014
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2547 -0.9050 -0.1423 0.5655 8.0179
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.21270 0.4612
## scale(PANAS_P) 0.08237 0.2870 -0.14
## scale(PANAS_N) 0.05482 0.2341 -0.37 0.82
## Number of obs: 2024, groups: subID, 210
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.270975 0.073638 -3.680 0.000233 ***
## scale(PANAS_P) 0.130171 0.040121 3.244 0.001177 **
## scale(PANAS_N) 0.203885 0.036301 5.617 1.95e-08 ***
## numID 0.041642 0.004371 9.526 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANAS_P s(PANAS_N
## sc(PANAS_P) -0.096
## sc(PANAS_N) -0.121 0.733
## numID -0.807 0.033 -0.025
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0420052 (tol = 0.002, component 1)m<-glmer(outdegree ~ scale(PANAS_1) + numID + ( scale(PANAS_1) | subID), data=fullData,family="poisson")
summary(m)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: outdegree ~ scale(PANAS_1) + numID + (scale(PANAS_1) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7382.6 7416.3 -3685.3 7370.6 2020
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1696 -0.9468 -0.1457 0.6021 8.5283
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.2123 0.4607
## scale(PANAS_1) 0.0365 0.1910 0.09
## Number of obs: 2026, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.219332 0.072973 -3.006 0.00265 **
## scale(PANAS_1) -0.005384 0.028583 -0.188 0.85059
## numID 0.038786 0.004372 8.871 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_1) -0.006
## numID -0.811 0.029m<-glmer(outdegree ~ scale(PANAS_2) + numID + ( scale(PANAS_2) | subID), data=fullData,family="poisson")
summary(m)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: outdegree ~ scale(PANAS_2) + numID + (scale(PANAS_2) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7360.3 7394.0 -3674.2 7348.3 2014
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2057 -0.9420 -0.1321 0.5946 8.5392
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.21560 0.4643
## scale(PANAS_2) 0.03714 0.1927 0.11
## Number of obs: 2020, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.223538 0.073380 -3.046 0.00232 **
## scale(PANAS_2) -0.012244 0.028681 -0.427 0.66946
## numID 0.038849 0.004397 8.835 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_2) 0.002
## numID -0.811 0.034m<-glmer(outdegree ~ scale(PANAS_3) + numID + ( scale(PANAS_3) | subID), data=fullData,family="poisson")
summary(m)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: outdegree ~ scale(PANAS_3) + numID + (scale(PANAS_3) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7388.5 7422.1 -3688.2 7376.5 2018
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2179 -0.9614 -0.1375 0.6147 8.7183
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.2116 0.460
## scale(PANAS_3) 0.0279 0.167 0.04
## Number of obs: 2024, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.215610 0.072760 -2.963 0.00304 **
## scale(PANAS_3) -0.007147 0.026949 -0.265 0.79086
## numID 0.038851 0.004364 8.903 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_3) -0.011
## numID -0.812 0.022m<-glmer(outdegree ~ scale(PANAS_4) + numID + ( scale(PANAS_4) | subID), data=fullData,family="poisson")## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00277919 (tol = 0.002, component 1)summary(m)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: outdegree ~ scale(PANAS_4) + numID + (scale(PANAS_4) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7355.5 7389.2 -3671.8 7343.5 2010
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1589 -0.9462 -0.1283 0.6123 8.6525
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.2165 0.4653
## scale(PANAS_4) 0.0315 0.1775 0.05
## Number of obs: 2016, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.210564 0.073286 -2.873 0.00406 **
## scale(PANAS_4) -0.010645 0.027558 -0.386 0.69929
## numID 0.038464 0.004405 8.733 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_4) 0.000
## numID -0.811 0.019
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00277919 (tol = 0.002, component 1)m<-glmer(outdegree ~ scale(PANAS_5) + numID + ( scale(PANAS_5) | subID), data=fullData,family="poisson")## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00225143 (tol = 0.002, component 1)summary(m)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: outdegree ~ scale(PANAS_5) + numID + (scale(PANAS_5) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7326.7 7360.3 -3657.4 7314.7 2007
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2607 -0.9321 -0.1393 0.6102 8.3581
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.20451 0.4522
## scale(PANAS_5) 0.05001 0.2236 0.02
## Number of obs: 2013, groups: subID, 210
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.222421 0.072794 -3.055 0.00225 **
## scale(PANAS_5) 0.012754 0.030668 0.416 0.67749
## numID 0.038413 0.004343 8.844 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_5) -0.035
## numID -0.811 0.037
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00225143 (tol = 0.002, component 1)m<-glmer(outdegree ~ scale(PANAS_6) + numID + ( scale(PANAS_6) | subID), data=fullData,family="poisson")
summary(m)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: outdegree ~ scale(PANAS_6) + numID + (scale(PANAS_6) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7289.7 7323.3 -3638.8 7277.7 1998
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1622 -0.9458 -0.1438 0.5814 10.2155
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.22693 0.4764
## scale(PANAS_6) 0.02762 0.1662 -0.27
## Number of obs: 2004, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.208671 0.073557 -2.837 0.00456 **
## scale(PANAS_6) 0.067077 0.026218 2.558 0.01051 *
## numID 0.038362 0.004439 8.642 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_6) -0.056
## numID -0.807 -0.032m<-glmer(outdegree ~ scale(PANAS_7) + numID + ( scale(PANAS_7) | subID), data=fullData,family="poisson")## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00227512 (tol = 0.002, component 1)summary(m)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: outdegree ~ scale(PANAS_7) + numID + (scale(PANAS_7) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7332.8 7366.5 -3660.4 7320.8 2009
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1972 -0.9538 -0.1640 0.6094 9.7612
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.22509 0.4744
## scale(PANAS_7) 0.02393 0.1547 -0.32
## Number of obs: 2015, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.223109 0.073441 -3.038 0.002382 **
## scale(PANAS_7) 0.085580 0.025708 3.329 0.000872 ***
## numID 0.039254 0.004401 8.919 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_7) -0.074
## numID -0.806 -0.032
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00227512 (tol = 0.002, component 1)m<-glmer(outdegree ~ scale(PANAS_8) + numID + ( scale(PANAS_8) | subID), data=fullData,family="poisson")
summary(m)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: outdegree ~ scale(PANAS_8) + numID + (scale(PANAS_8) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7345.6 7379.2 -3666.8 7333.6 2011
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2594 -0.9539 -0.1602 0.5810 9.9169
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.22548 0.4748
## scale(PANAS_8) 0.02235 0.1495 -0.42
## Number of obs: 2017, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.227962 0.072882 -3.128 0.00176 **
## scale(PANAS_8) 0.107117 0.024562 4.361 1.29e-05 ***
## numID 0.040131 0.004345 9.237 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_8) -0.088
## numID -0.804 -0.066m<-glmer(outdegree ~ scale(PANAS_9) + numID + ( scale(PANAS_9) | subID), data=fullData,family="poisson")
summary(m)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: outdegree ~ scale(PANAS_9) + numID + (scale(PANAS_9) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7321.1 7354.7 -3654.5 7309.1 2012
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2537 -0.9417 -0.1545 0.5878 9.8810
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.22060 0.4697
## scale(PANAS_9) 0.02037 0.1427 -0.35
## Number of obs: 2018, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.217340 0.072759 -2.987 0.002816 **
## scale(PANAS_9) 0.089319 0.024566 3.636 0.000277 ***
## numID 0.038945 0.004356 8.940 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_9) -0.071
## numID -0.807 -0.048m<-glmer(outdegree ~ scale(PANAS_10) + numID + ( scale(PANAS_10) | subID), data=fullData,family="poisson")
summary(m)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: outdegree ~ scale(PANAS_10) + numID + (scale(PANAS_10) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7333.4 7367.0 -3660.7 7321.4 2009
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0771 -0.9399 -0.1344 0.6036 10.4137
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.21964 0.4687
## scale(PANAS_10) 0.02394 0.1547 -0.42
## Number of obs: 2015, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.222271 0.072005 -3.087 0.00202 **
## scale(PANAS_10) 0.113663 0.025030 4.541 5.59e-06 ***
## numID 0.039908 0.004289 9.305 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## s(PANAS_10) -0.078
## numID -0.803 -0.074Indegree
More positive, more experiences cause an experience
m<-glmer(indegree ~ scale(PANAS_P) + scale(PANAS_N) + numID + ( scale(PANAS_P) + scale(PANAS_N) | subID), data=fullData,family="poisson")## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0150649 (tol = 0.002, component 1)summary(m)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula:
## indegree ~ scale(PANAS_P) + scale(PANAS_N) + numID + (scale(PANAS_P) +
## scale(PANAS_N) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 6999.3 7055.5 -3489.7 6979.3 2014
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7296 -0.6425 -0.1311 0.2854 8.9860
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.20783 0.4559
## scale(PANAS_P) 0.03141 0.1772 -0.15
## scale(PANAS_N) 0.03976 0.1994 0.02 0.63
## Number of obs: 2024, groups: subID, 210
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.207496 0.073023 -2.842 0.00449 **
## scale(PANAS_P) 0.098100 0.034287 2.861 0.00422 **
## scale(PANAS_N) 0.021779 0.035710 0.610 0.54194
## numID 0.038190 0.004422 8.637 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANAS_P s(PANAS_N
## sc(PANAS_P) -0.062
## sc(PANAS_N) -0.068 0.648
## numID -0.813 0.007 0.090
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0150649 (tol = 0.002, component 1)m<-glmer(indegree ~ scale(PANAS_1) + numID + ( scale(PANAS_1) | subID), data=fullData,family="poisson")
summary(m)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: indegree ~ scale(PANAS_1) + numID + (scale(PANAS_1) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7057.8 7091.5 -3522.9 7045.8 2020
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3746 -0.6618 -0.1311 0.2898 10.6200
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.21300 0.4615
## scale(PANAS_1) 0.02068 0.1438 -0.17
## Number of obs: 2026, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.193577 0.072274 -2.678 0.0074 **
## scale(PANAS_1) 0.065117 0.025886 2.516 0.0119 *
## numID 0.037734 0.004356 8.662 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_1) -0.018
## numID -0.811 -0.059m<-glmer(indegree ~ scale(PANAS_2) + numID + ( scale(PANAS_2) | subID), data=fullData,family="poisson")
summary(m)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: indegree ~ scale(PANAS_2) + numID + (scale(PANAS_2) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7017.8 7051.5 -3502.9 7005.8 2014
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3750 -0.6805 -0.1314 0.2929 9.5407
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.20989 0.4581
## scale(PANAS_2) 0.03127 0.1768 -0.20
## Number of obs: 2020, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.193787 0.072165 -2.685 0.00725 **
## scale(PANAS_2) 0.069461 0.027994 2.481 0.01309 *
## numID 0.037141 0.004341 8.556 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_2) -0.018
## numID -0.809 -0.081m<-glmer(indegree ~ scale(PANAS_3) + numID + ( scale(PANAS_3) | subID), data=fullData,family="poisson")
summary(m)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: indegree ~ scale(PANAS_3) + numID + (scale(PANAS_3) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7020.7 7054.4 -3504.3 7008.7 2018
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3803 -0.6669 -0.1259 0.2769 10.5421
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.21454 0.4632
## scale(PANAS_3) 0.02466 0.1570 -0.17
## Number of obs: 2024, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.199603 0.072439 -2.755 0.005861 **
## scale(PANAS_3) 0.090778 0.026861 3.379 0.000726 ***
## numID 0.037541 0.004365 8.601 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_3) -0.032
## numID -0.809 -0.056m<-glmer(indegree ~ scale(PANAS_4) + numID + ( scale(PANAS_4) | subID), data=fullData,family="poisson")
summary(m)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: indegree ~ scale(PANAS_4) + numID + (scale(PANAS_4) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7002.3 7036.0 -3495.2 6990.3 2010
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3712 -0.7003 -0.1421 0.2903 10.5508
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.21145 0.4598
## scale(PANAS_4) 0.02809 0.1676 -0.27
## Number of obs: 2016, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.196896 0.072056 -2.733 0.00628 **
## scale(PANAS_4) 0.083715 0.027408 3.054 0.00225 **
## numID 0.037457 0.004321 8.669 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_4) -0.022
## numID -0.808 -0.097m<-glmer(indegree ~ scale(PANAS_5) + numID + ( scale(PANAS_5) | subID), data=fullData,family="poisson")## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00524094 (tol = 0.002, component 1)summary(m)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: indegree ~ scale(PANAS_5) + numID + (scale(PANAS_5) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 6983.8 7017.5 -3485.9 6971.8 2007
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3954 -0.6967 -0.1409 0.2874 9.6745
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.21323 0.4618
## scale(PANAS_5) 0.02621 0.1619 -0.16
## Number of obs: 2013, groups: subID, 210
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.212147 0.072628 -2.921 0.003489 **
## scale(PANAS_5) 0.103939 0.026837 3.873 0.000108 ***
## numID 0.038484 0.004355 8.837 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_5) -0.065
## numID -0.809 -0.013
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00524094 (tol = 0.002, component 1)m<-glmer(indegree ~ scale(PANAS_6) + numID + ( scale(PANAS_6) | subID), data=fullData,family="poisson")## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00247089 (tol = 0.002, component 1)summary(m)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: indegree ~ scale(PANAS_6) + numID + (scale(PANAS_6) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 6962.3 6995.9 -3475.1 6950.3 1998
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4588 -0.6833 -0.1260 0.3078 9.0008
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.2118 0.4602
## scale(PANAS_6) 0.0233 0.1526 0.11
## Number of obs: 2004, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.194635 0.072596 -2.681 0.00734 **
## scale(PANAS_6) -0.051159 0.026512 -1.930 0.05365 .
## numID 0.037377 0.004391 8.512 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_6) -0.020
## numID -0.812 0.085
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00247089 (tol = 0.002, component 1)m<-glmer(indegree ~ scale(PANAS_7) + numID + ( scale(PANAS_7) | subID), data=fullData,family="poisson")
summary(m)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: indegree ~ scale(PANAS_7) + numID + (scale(PANAS_7) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7024.0 7057.7 -3506.0 7012.0 2009
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2003 -0.6976 -0.1192 0.2948 8.8923
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.22212 0.4713
## scale(PANAS_7) 0.02101 0.1450 0.06
## Number of obs: 2015, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.193717 0.073828 -2.624 0.00869 **
## scale(PANAS_7) -0.017714 0.026404 -0.671 0.50229
## numID 0.037200 0.004477 8.309 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_7) -0.043
## numID -0.812 0.084m<-glmer(indegree ~ scale(PANAS_8) + numID + ( scale(PANAS_8) | subID), data=fullData,family="poisson")
summary(m)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: indegree ~ scale(PANAS_8) + numID + (scale(PANAS_8) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7025.2 7058.8 -3506.6 7013.2 2011
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2262 -0.6747 -0.1146 0.3114 9.3700
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.2174 0.4663
## scale(PANAS_8) 0.0218 0.1477 0.16
## Number of obs: 2017, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.188103 0.073252 -2.568 0.0102 *
## scale(PANAS_8) -0.028800 0.026068 -1.105 0.2693
## numID 0.036948 0.004441 8.320 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_8) -0.048
## numID -0.813 0.118m<-glmer(indegree ~ scale(PANAS_9) + numID + ( scale(PANAS_9) | subID), data=fullData,family="poisson")
summary(m)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: indegree ~ scale(PANAS_9) + numID + (scale(PANAS_9) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7035.7 7069.4 -3511.9 7023.7 2012
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2298 -0.6648 -0.1104 0.2917 9.3696
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.2173 0.4662
## scale(PANAS_9) 0.0234 0.1530 0.12
## Number of obs: 2018, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.19218 0.07328 -2.623 0.00873 **
## scale(PANAS_9) -0.02539 0.02613 -0.972 0.33127
## numID 0.03747 0.00444 8.439 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## sc(PANAS_9) -0.035
## numID -0.813 0.092m<-glmer(indegree ~ scale(PANAS_10) + numID + ( scale(PANAS_10) | subID), data=fullData,family="poisson")
summary(m)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: indegree ~ scale(PANAS_10) + numID + (scale(PANAS_10) | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 7009.5 7043.2 -3498.8 6997.5 2009
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4484 -0.6821 -0.1305 0.2984 10.0272
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.21470 0.4634
## scale(PANAS_10) 0.01904 0.1380 0.09
## Number of obs: 2015, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.190824 0.073199 -2.607 0.00914 **
## scale(PANAS_10) -0.024909 0.025675 -0.970 0.33197
## numID 0.037077 0.004458 8.317 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PANA
## s(PANAS_10) -0.052
## numID -0.814 0.110H2: People will be more certain in memories with more downstream dependents.
Causing more experiences and being caused by more experiences is associated with greater certainty in experience, but causing is a stronger effect.
Using strength/similarity is stronger effect.
m<-lmer(scale(Cert) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## scale(Cert) ~ scale(outdegree) + scale(indegree) + numID + scale(length) +
## (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 5031.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6090 -0.5059 0.1124 0.5878 2.8176
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.307234 0.55429
## scale(outdegree) 0.057874 0.24057 -0.25
## scale(indegree) 0.006803 0.08248 -0.30 -0.19
## Residual 0.597374 0.77290
## Number of obs: 1978, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.477e-01 7.699e-02 2.521e+02 1.918 0.05623 .
## scale(outdegree) 9.801e-02 3.474e-02 8.611e+01 2.821 0.00594 **
## scale(indegree) 5.612e-02 2.643e-02 1.985e+01 2.124 0.04648 *
## numID -7.075e-03 5.004e-03 1.778e+02 -1.414 0.15918
## scale(length) -1.175e-01 2.153e-02 1.949e+03 -5.457 5.45e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.193
## scale(ndgr) 0.139 -0.114
## numID -0.807 -0.229 -0.180
## scal(lngth) -0.081 -0.074 0.118 0.049m<-lmer(scale(Cert) ~ scale(strengthIn) + scale(strengthOut) + ( scale(strengthIn) + scale(strengthOut) | subID), data=fullData)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## scale(Cert) ~ scale(strengthIn) + scale(strengthOut) + (scale(strengthIn) +
## scale(strengthOut) | subID)
## Data: fullData
##
## REML criterion at convergence: 5182.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7369 -0.5148 0.1246 0.5897 2.6249
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.29490 0.5430
## scale(strengthIn) 0.01240 0.1113 -0.23
## scale(strengthOut) 0.02925 0.1710 -0.43 -0.26
## Residual 0.60773 0.7796
## Number of obs: 2044, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.05506 0.04389 206.39256 1.255 0.211075
## scale(strengthIn) 0.09384 0.02863 24.99336 3.278 0.003068 **
## scale(strengthOut) 0.11503 0.02895 60.50874 3.974 0.000191 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(I)
## scl(strngI) 0.025
## scl(strngO) -0.085 -0.275H3: Memories with more dependents will be more clearly defined and accessible.
Experiences causing more experiences are more predictive of clarity than experiences caused by more experiences
m<-lmer( scale(Clear) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## scale(Clear) ~ scale(outdegree) + scale(indegree) + numID + scale(length) +
## (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 5140.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.2286 -0.4757 0.1554 0.6171 3.2107
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.289103 0.53768
## scale(outdegree) 0.034093 0.18464 -0.16
## scale(indegree) 0.005779 0.07602 -0.16 -0.57
## Residual 0.630087 0.79378
## Number of obs: 1999, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.890e-01 7.424e-02 2.564e+02 2.546 0.0115 *
## scale(outdegree) 7.679e-02 3.105e-02 8.094e+01 2.473 0.0155 *
## scale(indegree) 3.706e-02 2.571e-02 1.740e+01 1.441 0.1673
## numID -9.836e-03 4.836e-03 1.801e+02 -2.034 0.0434 *
## scale(length) -1.188e-01 2.180e-02 1.974e+03 -5.449 5.69e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.176
## scale(ndgr) 0.118 -0.221
## numID -0.801 -0.182 -0.142
## scal(lngth) -0.081 -0.077 0.129 0.049m<-lmer( scale(Clear) ~ scale(strengthIn) + scale(strengthOut) + numID + scale(length) + ( scale(strengthIn) + scale(strengthOut) | subID), data=fullData)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00223588 (tol = 0.002, component 1)summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(Clear) ~ scale(strengthIn) + scale(strengthOut) + numID +
## scale(length) + (scale(strengthIn) + scale(strengthOut) | subID)
## Data: fullData
##
## REML criterion at convergence: 5131.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3406 -0.4673 0.1358 0.6127 3.2332
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.286449 0.53521
## scale(strengthIn) 0.008327 0.09125 -0.22
## scale(strengthOut) 0.018026 0.13426 -0.38 -0.30
## Residual 0.632690 0.79542
## Number of obs: 1999, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.191e-01 7.461e-02 2.635e+02 2.936 0.00362 **
## scale(strengthIn) 5.984e-02 2.784e-02 2.935e+01 2.150 0.03996 *
## scale(strengthOut) 1.200e-01 2.802e-02 6.655e+01 4.282 6.08e-05 ***
## numID -1.143e-02 4.831e-03 1.891e+02 -2.367 0.01897 *
## scale(length) -1.176e-01 2.167e-02 1.958e+03 -5.428 6.40e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(I) scl(O) numID
## scl(strngI) 0.140
## scl(strngO) 0.172 -0.229
## numID -0.807 -0.163 -0.244
## scal(lngth) -0.074 0.135 -0.065 0.043
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00223588 (tol = 0.002, component 1)H5: Memories with more dependents will be more fundamental to how people see themselves, and if they were changed, would change the person.
The number of an experiences of causes, but not what it is caused by, predict how fundamental an experience is.
m<-lmer( scale(Fund) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## scale(Fund) ~ scale(outdegree) + scale(indegree) + numID + scale(length) +
## (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 5206.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2467 -0.5500 0.1269 0.6305 2.7223
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.23053 0.48013
## scale(outdegree) 0.01731 0.13157 -0.62
## scale(indegree) 0.00201 0.04484 0.11 0.34
## Residual 0.67263 0.82014
## Number of obs: 2001, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.030e-01 7.013e-02 2.787e+02 4.321 2.17e-05 ***
## scale(outdegree) 2.411e-01 2.681e-02 6.215e+01 8.990 7.56e-13 ***
## scale(indegree) 5.018e-02 2.397e-02 6.440e+00 2.094 0.077981 .
## numID -1.653e-02 4.445e-03 1.902e+02 -3.719 0.000263 ***
## scale(length) 4.230e-02 2.193e-02 1.854e+03 1.929 0.053890 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.213
## scale(ndgr) 0.112 -0.019
## numID -0.811 -0.345 -0.077
## scal(lngth) -0.100 -0.089 0.130 0.070m<-lmer( scale(Fund) ~ scale(strengthIn) + scale(strengthOut) + numID + scale(length) + ( scale(strengthIn) + scale(strengthOut) | subID), data=fullData)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(Fund) ~ scale(strengthIn) + scale(strengthOut) + numID +
## scale(length) + (scale(strengthIn) + scale(strengthOut) | subID)
## Data: fullData
##
## REML criterion at convergence: 5172.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3264 -0.5650 0.1118 0.6422 2.8645
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.231291 0.48093
## scale(strengthIn) 0.006982 0.08356 0.12
## scale(strengthOut) 0.013599 0.11661 -0.67 0.66
## Residual 0.659268 0.81195
## Number of obs: 2001, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.393e-01 7.025e-02 2.909e+02 4.830 2.21e-06 ***
## scale(strengthIn) 7.023e-02 2.714e-02 4.316e+01 2.588 0.0131 *
## scale(strengthOut) 2.688e-01 2.596e-02 9.901e+01 10.357 < 2e-16 ***
## numID -1.866e-02 4.431e-03 1.980e+02 -4.211 3.85e-05 ***
## scale(length) 5.370e-02 2.172e-02 1.945e+03 2.473 0.0135 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(I) scl(O) numID
## scl(strngI) 0.130
## scl(strngO) 0.247 0.070
## numID -0.808 -0.049 -0.379
## scal(lngth) -0.087 0.131 -0.066 0.059
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Important principal factor (Changed me, Fundamental, Representative)
m<-lmer(scale(PCAimp) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(PCAimp) ~ scale(outdegree) + scale(indegree) + numID +
## scale(length) + (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 5053.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3852 -0.5216 0.0902 0.6224 2.7075
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.268839 0.51850
## scale(outdegree) 0.016052 0.12670 -0.60
## scale(indegree) 0.007867 0.08869 0.03 0.07
## Residual 0.608755 0.78023
## Number of obs: 2001, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.677e-01 7.242e-02 2.756e+02 5.078 7.03e-07 ***
## scale(outdegree) 2.304e-01 2.594e-02 7.478e+01 8.879 2.58e-13 ***
## scale(indegree) 6.813e-02 2.726e-02 2.695e+01 2.499 0.0188 *
## numID -1.982e-02 4.664e-03 1.978e+02 -4.249 3.31e-05 ***
## scale(length) 3.321e-02 2.115e-02 1.903e+03 1.570 0.1165
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.199
## scale(ndgr) 0.130 -0.051
## numID -0.805 -0.332 -0.091
## scal(lngth) -0.091 -0.086 0.118 0.063m<-lmer(scale(PCAimp) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(PCAimp) ~ scale(strengthOut) + scale(strengthIn) + numID +
## scale(length) + (scale(strengthOut) + scale(strengthIn) | subID)
## Data: fullData
##
## REML criterion at convergence: 5011.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4462 -0.5297 0.0793 0.6167 2.7160
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.27011 0.5197
## scale(strengthOut) 0.01080 0.1039 -0.73
## scale(strengthIn) 0.01076 0.1037 0.24 0.49
## Residual 0.59734 0.7729
## Number of obs: 2001, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.079e-01 7.178e-02 2.872e+02 5.682 3.27e-08 ***
## scale(strengthOut) 2.607e-01 2.420e-02 1.282e+02 10.773 < 2e-16 ***
## scale(strengthIn) 9.057e-02 2.829e-02 4.022e+01 3.201 0.00267 **
## numID -2.222e-02 4.596e-03 2.049e+02 -4.833 2.63e-06 ***
## scale(length) 4.612e-02 2.086e-02 1.960e+03 2.211 0.02717 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(O) scl(I) numID
## scl(strngO) 0.247
## scl(strngI) 0.121 0.065
## numID -0.798 -0.399 0.000
## scal(lngth) -0.081 -0.064 0.130 0.056
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')H6: Memories with more dependents will be more important to the person.
To Self
Experiences that cause more experiences are perceived as important to self, but not experiences that are caused by more experiences.
m<-lmer( scale(IM) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## scale(IM) ~ scale(outdegree) + scale(indegree) + numID + scale(length) +
## (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 5312.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4772 -0.4132 0.2034 0.5973 2.5891
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.1893444 0.43514
## scale(outdegree) 0.0246494 0.15700 -0.37
## scale(indegree) 0.0004888 0.02211 -0.98 0.15
## Residual 0.7178590 0.84727
## Number of obs: 2002, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.489e-01 6.778e-02 3.037e+02 5.148 4.75e-07 ***
## scale(outdegree) 1.583e-01 2.951e-02 9.646e+01 5.363 5.60e-07 ***
## scale(indegree) 7.053e-02 2.183e-02 1.325e+03 3.231 0.00126 **
## numID -1.917e-02 4.238e-03 1.972e+02 -4.523 1.05e-05 ***
## scale(length) -6.436e-02 2.254e-02 1.922e+03 -2.855 0.00434 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.205
## scale(ndgr) 0.143 -0.093
## numID -0.820 -0.264 -0.205
## scal(lngth) -0.102 -0.085 0.132 0.068
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m<-lmer( scale(IM) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(IM) ~ scale(strengthOut) + scale(strengthIn) + numID +
## scale(length) + (scale(strengthOut) + scale(strengthIn) | subID)
## Data: fullData
##
## REML criterion at convergence: 5272.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4657 -0.4128 0.1911 0.5905 2.5754
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.182713 0.42745
## scale(strengthOut) 0.023005 0.15167 -0.55
## scale(strengthIn) 0.001829 0.04277 -0.16 0.91
## Residual 0.703560 0.83879
## Number of obs: 2002, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.889e-01 6.728e-02 3.229e+02 5.780 1.76e-08 ***
## scale(strengthOut) 1.966e-01 2.923e-02 9.618e+01 6.725 1.25e-09 ***
## scale(strengthIn) 7.710e-02 2.432e-02 7.275e+01 3.171 0.00223 **
## numID -2.149e-02 4.156e-03 2.061e+02 -5.171 5.50e-07 ***
## scale(length) -5.727e-02 2.223e-02 1.919e+03 -2.576 0.01006 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(O) scl(I) numID
## scl(strngO) 0.238
## scl(strngI) 0.138 0.014
## numID -0.821 -0.341 -0.120
## scal(lngth) -0.096 -0.069 0.135 0.064
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')To Others
Experiences that cause more experiences are perceived as important to others, but not experiences that are caused by more experiences.
m<-lmer( scale(IO) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## scale(IO) ~ scale(outdegree) + scale(indegree) + numID + scale(length) +
## (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 5386.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2061 -0.6042 0.1366 0.6781 2.3626
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.18664 0.4320
## scale(outdegree) 0.01514 0.1231 -0.48
## scale(indegree) 0.01821 0.1349 -0.39 -0.04
## Residual 0.74699 0.8643
## Number of obs: 1999, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.987e-01 6.882e-02 2.856e+02 2.887 0.00418 **
## scale(outdegree) 1.265e-01 2.767e-02 5.062e+01 4.572 3.14e-05 ***
## scale(indegree) 4.480e-02 3.230e-02 2.837e+01 1.387 0.17627
## numID -1.081e-02 4.271e-03 1.812e+02 -2.530 0.01226 *
## scale(length) -4.575e-02 2.294e-02 1.873e+03 -1.995 0.04622 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.185
## scale(ndgr) 0.178 -0.135
## numID -0.826 -0.274 -0.235
## scal(lngth) -0.099 -0.091 0.104 0.066m<-lmer( scale(IO) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(IO) ~ scale(strengthOut) + scale(strengthIn) + numID +
## scale(length) + (scale(strengthOut) + scale(strengthIn) | subID)
## Data: fullData
##
## REML criterion at convergence: 5374.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2080 -0.6087 0.1441 0.6728 2.5154
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.18470 0.4298
## scale(strengthOut) 0.01045 0.1022 -0.56
## scale(strengthIn) 0.01748 0.1322 -0.32 0.32
## Residual 0.74478 0.8630
## Number of obs: 1999, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.324e-01 6.886e-02 2.932e+02 3.375 0.000838 ***
## scale(strengthOut) 1.422e-01 2.674e-02 4.664e+01 5.320 2.88e-06 ***
## scale(strengthIn) 7.125e-02 3.283e-02 3.189e+01 2.170 0.037542 *
## numID -1.272e-02 4.247e-03 1.863e+02 -2.995 0.003122 **
## scale(length) -3.765e-02 2.283e-02 1.893e+03 -1.649 0.099224 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(O) scl(I) numID
## scl(strngO) 0.205
## scl(strngI) 0.191 -0.074
## numID -0.825 -0.292 -0.211
## scal(lngth) -0.088 -0.068 0.111 0.056More Important to Self than Others
No evidence
fullData$ImpDiff <- (fullData$IM-fullData$IO)
m<-lmer( scale(ImpDiff) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(ImpDiff) ~ scale(strengthOut) + scale(strengthIn) + numID +
## scale(length) + (scale(strengthOut) + scale(strengthIn) | subID)
## Data: fullData
##
## REML criterion at convergence: 5523
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3018 -0.5933 -0.2058 0.5344 3.5220
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.15512 0.3939
## scale(strengthOut) 0.01130 0.1063 -0.19
## scale(strengthIn) 0.01862 0.1364 -0.13 -0.29
## Residual 0.81724 0.9040
## Number of obs: 1999, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.053e-02 6.595e-02 2.824e+02 0.766 0.444
## scale(strengthOut) -9.043e-03 2.850e-02 6.654e+01 -0.317 0.752
## scale(strengthIn) -7.300e-03 3.445e-02 2.686e+01 -0.212 0.834
## numID -3.020e-03 4.014e-03 1.675e+02 -0.752 0.453
## scale(length) -2.838e-03 2.357e-02 1.831e+03 -0.120 0.904
##
## Correlation of Fixed Effects:
## (Intr) scl(O) scl(I) numID
## scl(strngO) 0.170
## scl(strngI) 0.177 -0.231
## numID -0.821 -0.185 -0.165
## scal(lngth) -0.092 -0.067 0.120 0.057m<-lmer( scale(ImpDiff) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(ImpDiff) ~ scale(outdegree) + scale(indegree) + numID +
## scale(length) + (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 5522
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2377 -0.5761 -0.2066 0.5357 3.5269
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.15463 0.3932
## scale(outdegree) 0.01526 0.1235 -0.19
## scale(indegree) 0.02248 0.1499 -0.21 -0.40
## Residual 0.81309 0.9017
## Number of obs: 1999, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.008e-02 6.599e-02 2.830e+02 0.759 0.449
## scale(outdegree) -1.517e-02 2.885e-02 6.510e+01 -0.526 0.601
## scale(indegree) 2.418e-03 3.472e-02 2.524e+01 0.070 0.945
## numID -2.986e-03 4.031e-03 1.665e+02 -0.741 0.460
## scale(length) 7.946e-04 2.361e-02 1.825e+03 0.034 0.973
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.169
## scale(ndgr) 0.171 -0.227
## numID -0.824 -0.190 -0.189
## scal(lngth) -0.103 -0.088 0.107 0.067H11: People think more often about memories with more memories causing them.
Experiences with more causes and caused by more are reflected on more frequently. Perhaps some stronger effects for causing more.
m<-lmer( scale(Often) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## scale(Often) ~ scale(outdegree) + scale(indegree) + numID + scale(length) +
## (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 5125.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5447 -0.6743 -0.0551 0.6695 2.7754
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.280069 0.52922
## scale(outdegree) 0.041688 0.20418 0.03
## scale(indegree) 0.002115 0.04599 -1.00 -0.06
## Residual 0.645235 0.80326
## Number of obs: 1977, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.43322 0.07428 264.34044 5.832 1.59e-08 ***
## scale(outdegree) 0.12297 0.03281 64.99111 3.748 0.000381 ***
## scale(indegree) 0.07828 0.02169 276.24537 3.608 0.000366 ***
## numID -0.02422 0.00480 185.07745 -5.045 1.08e-06 ***
## scale(length) -0.10483 0.02210 1959.37290 -4.744 2.25e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.195
## scale(ndgr) 0.177 -0.074
## numID -0.802 -0.126 -0.307
## scal(lngth) -0.075 -0.074 0.132 0.038
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m<-lmer( scale(Often) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)## boundary (singular) fit: see help('isSingular')## Warning: Model failed to converge with 1 negative eigenvalue: -6.5e+02summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(Often) ~ scale(strengthOut) + scale(strengthIn) + numID +
## scale(length) + (scale(strengthOut) + scale(strengthIn) | subID)
## Data: fullData
##
## REML criterion at convergence: 5117.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5156 -0.6768 -0.0391 0.6767 2.8425
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 2.769e-01 0.5262348
## scale(strengthOut) 1.763e-07 0.0004199 -1.00
## scale(strengthIn) 1.115e-02 0.1055889 -0.38 0.38
## Residual 6.574e-01 0.8108015
## Number of obs: 1977, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.661e-01 7.334e-02 2.608e+02 6.356 9.22e-10 ***
## scale(strengthOut) 1.587e-01 2.158e-02 1.295e+03 7.353 3.41e-13 ***
## scale(strengthIn) 1.162e-01 2.919e-02 2.284e+01 3.980 0.000597 ***
## numID -2.585e-02 4.755e-03 1.869e+02 -5.437 1.68e-07 ***
## scale(length) -9.569e-02 2.175e-02 1.957e+03 -4.400 1.14e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(O) scl(I) numID
## scl(strngO) 0.129
## scl(strngI) 0.182 -0.141
## numID -0.802 -0.118 -0.230
## scal(lngth) -0.061 -0.043 0.132 0.029
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')H12: The more memories that depend on a given memory, the more people believe “This memory changed me”. Weaker effect for memories with many causes of it.
m<-lmer( scale(Chan) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## scale(Chan) ~ scale(outdegree) + scale(indegree) + numID + scale(length) +
## (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 5216.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5976 -0.5322 0.1484 0.6224 2.7493
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.2310589 0.48069
## scale(outdegree) 0.0135199 0.11628 -0.62
## scale(indegree) 0.0001786 0.01336 -0.18 0.88
## Residual 0.6790026 0.82402
## Number of obs: 2001, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.569e-01 7.019e-02 2.751e+02 5.085 6.81e-07 ***
## scale(outdegree) 2.149e-01 2.586e-02 8.309e+01 8.309 1.57e-12 ***
## scale(indegree) 4.684e-02 2.174e-02 1.191e+02 2.154 0.0332 *
## numID -1.975e-02 4.464e-03 1.889e+02 -4.424 1.64e-05 ***
## scale(length) 3.090e-02 2.198e-02 1.889e+03 1.405 0.1601
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.208
## scale(ndgr) 0.114 -0.039
## numID -0.812 -0.332 -0.113
## scal(lngth) -0.098 -0.090 0.140 0.068
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m<-lmer( scale(Chan) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(Chan) ~ scale(strengthOut) + scale(strengthIn) + numID +
## scale(length) + (scale(strengthOut) + scale(strengthIn) | subID)
## Data: fullData
##
## REML criterion at convergence: 5179.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7230 -0.5164 0.1368 0.6088 2.8039
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.234197 0.48394
## scale(strengthOut) 0.011446 0.10698 -0.66
## scale(strengthIn) 0.003429 0.05855 0.32 0.50
## Residual 0.664514 0.81518
## Number of obs: 2001, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.38996 0.06988 282.23489 5.580 5.63e-08 ***
## scale(strengthOut) 0.24773 0.02551 103.26253 9.710 3.19e-16 ***
## scale(strengthIn) 0.05136 0.02525 45.71799 2.034 0.0478 *
## numID -0.02195 0.00443 192.91915 -4.955 1.57e-06 ***
## scale(length) 0.04049 0.02176 1933.14650 1.861 0.0629 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(O) scl(I) numID
## scl(strngO) 0.233
## scl(strngI) 0.106 -0.012
## numID -0.805 -0.361 -0.019
## scal(lngth) -0.087 -0.066 0.143 0.058
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')H13: The more memories that depend on a given memory, the more certain that people feel this experience is representative of who they are.
People feel experiences with more causes are more representative of self. Similar, but weaker, effect for experiences caused by more experiences.
m<-lmer( scale(Rep) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## scale(Rep) ~ scale(outdegree) + scale(indegree) + numID + scale(length) +
## (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 5090.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3859 -0.5487 0.0731 0.6374 2.9952
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.25619 0.5062
## scale(outdegree) 0.01300 0.1140 -0.36
## scale(indegree) 0.01362 0.1167 -0.14 0.00
## Residual 0.62194 0.7886
## Number of obs: 2001, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.366e-01 7.179e-02 2.812e+02 4.688 4.30e-06 ***
## scale(outdegree) 1.562e-01 2.577e-02 6.217e+01 6.062 8.66e-08 ***
## scale(indegree) 9.478e-02 2.992e-02 4.393e+01 3.168 0.002793 **
## numID -1.786e-02 4.628e-03 1.982e+02 -3.860 0.000153 ***
## scale(length) 1.825e-02 2.139e-02 1.915e+03 0.853 0.393570
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.183
## scale(ndgr) 0.160 -0.073
## numID -0.806 -0.239 -0.152
## scal(lngth) -0.084 -0.083 0.113 0.054m<-lmer( scale(Rep) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(Rep) ~ scale(strengthOut) + scale(strengthIn) + numID +
## scale(length) + (scale(strengthOut) + scale(strengthIn) | subID)
## Data: fullData
##
## REML criterion at convergence: 5065.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4235 -0.5514 0.0586 0.6461 2.9458
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.253668 0.50365
## scale(strengthOut) 0.006451 0.08032 -0.66
## scale(strengthIn) 0.016450 0.12826 0.14 0.65
## Residual 0.617913 0.78607
## Number of obs: 2001, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.663e-01 7.107e-02 2.943e+02 5.154 4.68e-07 ***
## scale(strengthOut) 1.750e-01 2.302e-02 1.595e+02 7.602 2.36e-12 ***
## scale(strengthIn) 1.305e-01 3.091e-02 4.372e+01 4.220 0.000121 ***
## numID -1.911e-02 4.537e-03 2.032e+02 -4.211 3.81e-05 ***
## scale(length) 2.974e-02 2.114e-02 1.966e+03 1.407 0.159606
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(O) scl(I) numID
## scl(strngO) 0.237
## scl(strngI) 0.159 0.092
## numID -0.799 -0.343 -0.052
## scal(lngth) -0.078 -0.059 0.121 0.051
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Exploratory Analyses
Sentiment of memory description will be associated with dependencies
Combined
Experiences caused by more experiences have more qualitative positive sentiment.
m<-lmer( scale(vad_comp) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)## boundary (singular) fit: see help('isSingular')## Warning: Model failed to converge with 1 negative eigenvalue: -5.9e+01summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(vad_comp) ~ scale(outdegree) + scale(indegree) + numID +
## scale(length) + (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 6252.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1215 -0.2884 -0.1403 0.2995 2.9679
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.052467 0.22906
## scale(outdegree) 0.001345 0.03667 1.00
## scale(indegree) 0.018997 0.13783 0.54 0.54
## Residual 0.928431 0.96355
## Number of obs: 2215, groups: subID, 215
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.975e-02 5.256e-02 3.270e+02 1.708 0.08865 .
## scale(outdegree) 4.230e-03 2.449e-02 1.747e+02 0.173 0.86306
## scale(indegree) 9.365e-02 3.326e-02 3.969e+01 2.816 0.00754 **
## numID -4.418e-03 2.748e-03 1.137e+02 -1.608 0.11063
## scale(length) -1.513e-02 2.271e-02 1.373e+03 -0.666 0.50522
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.190
## scale(ndgr) 0.185 -0.050
## numID -0.828 -0.108 -0.020
## scal(lngth) -0.139 -0.108 0.086 0.149
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m<-lmer( scale(vad_comp) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)## boundary (singular) fit: see help('isSingular')## Warning: Model failed to converge with 1 negative eigenvalue: -3.9e+00summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(vad_comp) ~ scale(strengthOut) + scale(strengthIn) + numID +
## scale(length) + (scale(strengthOut) + scale(strengthIn) | subID)
## Data: fullData
##
## REML criterion at convergence: 6249.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3313 -0.2978 -0.1362 0.2809 2.9859
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.0580113 0.24086
## scale(strengthOut) 0.0009226 0.03037 1.00
## scale(strengthIn) 0.0201867 0.14208 0.53 0.53
## Residual 0.9241950 0.96135
## Number of obs: 2215, groups: subID, 215
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.412e-02 5.336e-02 3.320e+02 1.764 0.07867 .
## scale(strengthOut) -4.679e-03 2.474e-02 1.861e+02 -0.189 0.85021
## scale(strengthIn) 1.010e-01 3.413e-02 4.409e+01 2.958 0.00496 **
## numID -4.506e-03 2.822e-03 1.217e+02 -1.597 0.11292
## scale(length) -1.402e-02 2.269e-02 1.364e+03 -0.618 0.53667
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(O) scl(I) numID
## scl(strngO) 0.182
## scl(strngI) 0.199 -0.098
## numID -0.825 -0.108 -0.031
## scal(lngth) -0.123 -0.070 0.100 0.134
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Positive
Experiences with more experiences causing them are qualitatively more positive
m<-lmer( scale(vad_pos) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)## boundary (singular) fit: see help('isSingular')## Warning: Model failed to converge with 1 negative eigenvalue: -5.5e+00summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(vad_pos) ~ scale(outdegree) + scale(indegree) + numID +
## scale(length) + (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 6280.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4990 -0.5707 -0.4513 0.2449 4.9708
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.0308695 0.17570
## scale(outdegree) 0.0002493 0.01579 -1.00
## scale(indegree) 0.0135756 0.11651 0.00 0.00
## Residual 0.9512739 0.97533
## Number of obs: 2215, groups: subID, 215
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.235e-02 5.161e-02 4.055e+02 1.014 0.31100
## scale(outdegree) -1.533e-02 2.340e-02 1.365e+03 -0.655 0.51236
## scale(indegree) 8.794e-02 3.098e-02 3.174e+01 2.838 0.00784 **
## numID -2.678e-03 2.682e-03 1.587e+02 -0.998 0.31959
## scale(length) -5.457e-02 2.271e-02 1.451e+03 -2.403 0.01639 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.174
## scale(ndgr) 0.161 -0.192
## numID -0.863 -0.210 -0.121
## scal(lngth) -0.153 -0.133 0.077 0.159
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m<-lmer( scale(vad_pos) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)## boundary (singular) fit: see help('isSingular')## Warning: Model failed to converge with 1 negative eigenvalue: -1.2e+01summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(vad_pos) ~ scale(strengthOut) + scale(strengthIn) + numID +
## scale(length) + (scale(strengthOut) + scale(strengthIn) | subID)
## Data: fullData
##
## REML criterion at convergence: 6280
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4112 -0.5711 -0.4542 0.2452 4.9415
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.031845 0.17845
## scale(strengthOut) 0.000428 0.02069 -1.00
## scale(strengthIn) 0.008238 0.09077 -0.05 0.05
## Residual 0.953023 0.97623
## Number of obs: 2215, groups: subID, 215
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.653e-02 5.203e-02 4.084e+02 1.086 0.2780
## scale(strengthOut) -1.947e-02 2.385e-02 1.698e+03 -0.816 0.4144
## scale(strengthIn) 8.597e-02 2.921e-02 2.930e+01 2.943 0.0063 **
## numID -3.003e-03 2.714e-03 1.658e+02 -1.107 0.2701
## scale(length) -5.471e-02 2.268e-02 1.439e+03 -2.412 0.0160 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(O) scl(I) numID
## scl(strngO) 0.180
## scl(strngI) 0.164 -0.254
## numID -0.866 -0.221 -0.142
## scal(lngth) -0.143 -0.103 0.085 0.148
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Negative
No negative effects
m<-lmer( scale(vad_neg) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(vad_neg) ~ scale(outdegree) + scale(indegree) + numID +
## scale(length) + (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 6271.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2895 -0.4122 -0.2912 -0.1984 6.5233
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.064206 0.25339
## scale(outdegree) 0.005104 0.07144 0.28
## scale(indegree) 0.001228 0.03504 -0.97 -0.05
## Residual 0.932602 0.96571
## Number of obs: 2215, groups: subID, 215
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -6.519e-02 5.541e-02 3.214e+02 -1.176 0.240
## scale(outdegree) -2.064e-02 2.589e-02 8.614e+01 -0.797 0.427
## scale(indegree) -3.201e-03 2.329e-02 1.446e+02 -0.137 0.891
## numID 3.234e-03 3.043e-03 1.447e+02 1.063 0.290
## scale(length) -1.277e-03 2.331e-02 1.576e+03 -0.055 0.956
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.172
## scale(ndgr) 0.146 -0.187
## numID -0.846 -0.120 -0.210
## scal(lngth) -0.135 -0.107 0.099 0.131
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m<-lmer( scale(vad_neg) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(vad_neg) ~ scale(strengthOut) + scale(strengthIn) + numID +
## scale(length) + (scale(strengthOut) + scale(strengthIn) | subID)
## Data: fullData
##
## REML criterion at convergence: 6271.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2743 -0.4125 -0.2928 -0.1973 6.5147
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.066791 0.25844
## scale(strengthOut) 0.003819 0.06179 0.72
## scale(strengthIn) 0.001439 0.03793 -0.86 -0.27
## Residual 0.933385 0.96612
## Number of obs: 2215, groups: subID, 215
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -6.008e-02 5.533e-02 3.313e+02 -1.086 0.278
## scale(strengthOut) -1.216e-02 2.601e-02 9.089e+01 -0.468 0.641
## scale(strengthIn) 1.618e-03 2.431e-02 8.086e+01 0.067 0.947
## numID 3.063e-03 3.034e-03 1.505e+02 1.010 0.314
## scale(length) -2.765e-03 2.315e-02 1.542e+03 -0.119 0.905
##
## Correlation of Fixed Effects:
## (Intr) scl(O) scl(I) numID
## scl(strngO) 0.147
## scl(strngI) 0.165 -0.243
## numID -0.841 -0.049 -0.223
## scal(lngth) -0.121 -0.070 0.104 0.118
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Number of words
No effect of causes or caused by on number of words
m<-glmer(nwords ~ outdegree + indegree + numID + scale(length) + ( outdegree + indegree | subID), data=fullData, family="poisson")
summary(m)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: nwords ~ outdegree + indegree + numID + scale(length) + (outdegree +
## indegree | subID)
## Data: fullData
##
## AIC BIC logLik deviance df.resid
## 12504.6 12567.3 -6241.3 12482.6 2204
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.3076 -0.7071 -0.1331 0.5397 12.0926
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.568425 0.75394
## outdegree 0.002340 0.04838 0.12
## indegree 0.002723 0.05218 -0.38 0.26
## Number of obs: 2215, groups: subID, 215
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.9334800 0.0811461 23.827 <2e-16 ***
## outdegree -0.0123978 0.0077872 -1.592 0.111
## indegree -0.0005592 0.0090943 -0.061 0.951
## numID 0.0006189 0.0056419 0.110 0.913
## scale(length) 0.0862643 0.0097761 8.824 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) outdgr indegr numID
## outdegree -0.099
## indegree -0.081 0.116
## numID -0.738 0.044 -0.178
## scal(lngth) -0.034 -0.047 0.085 0.014Breadth
Experiences with more causes are more broad.
m<-lmer( scale(Breadth) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(Breadth) ~ scale(outdegree) + scale(indegree) + numID +
## scale(length) + (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 5255.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.38126 -0.64650 -0.00537 0.65203 2.99689
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.254937 0.50491
## scale(outdegree) 0.033977 0.18433 -0.08
## scale(indegree) 0.001751 0.04185 -0.01 0.94
## Residual 0.703320 0.83864
## Number of obs: 1975, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.435e-01 7.291e-02 2.532e+02 3.339 0.000967 ***
## scale(outdegree) 9.815e-02 3.180e-02 5.899e+01 3.086 0.003086 **
## scale(indegree) -2.257e-02 2.359e-02 4.733e+00 -0.957 0.385073
## numID -1.430e-02 4.654e-03 1.716e+02 -3.072 0.002472 **
## scale(length) 4.908e-02 2.276e-02 1.916e+03 2.157 0.031163 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.224
## scale(ndgr) 0.144 0.113
## numID -0.800 -0.187 -0.114
## scal(lngth) -0.086 -0.075 0.134 0.051m<-lmer( scale(Breadth) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)## boundary (singular) fit: see help('isSingular')## Warning: Model failed to converge with 1 negative eigenvalue: -1.6e+03summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(Breadth) ~ scale(strengthOut) + scale(strengthIn) + numID +
## scale(length) + (scale(strengthOut) + scale(strengthIn) | subID)
## Data: fullData
##
## REML criterion at convergence: 5276
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3666 -0.6810 -0.0020 0.6681 3.1794
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.2663596 0.51610
## scale(strengthOut) 0.0004799 0.02191 1.00
## scale(strengthIn) 0.0055609 0.07457 0.24 0.24
## Residual 0.7270389 0.85267
## Number of obs: 1975, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.239e-01 7.090e-02 2.030e+02 3.158 0.00183 **
## scale(strengthOut) 6.527e-02 2.299e-02 4.132e+02 2.840 0.00474 **
## scale(strengthIn) 7.710e-03 2.789e-02 2.932e+00 0.276 0.80054
## numID -1.265e-02 4.571e-03 1.580e+02 -2.767 0.00633 **
## scale(length) 6.804e-02 2.257e-02 1.890e+03 3.015 0.00261 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(O) scl(I) numID
## scl(strngO) 0.125
## scl(strngI) 0.135 -0.107
## numID -0.782 -0.044 -0.060
## scal(lngth) -0.064 -0.036 0.152 0.030
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Distinctness
Experiences with more causes are perceived as more distinct/different.
m<-lmer( scale(Dist) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## scale(Dist) ~ scale(outdegree) + scale(indegree) + numID + scale(length) +
## (scale(outdegree) + scale(indegree) | subID)
## Data: fullData
##
## REML criterion at convergence: 5135.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9072 -0.5359 0.1170 0.6383 2.6875
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.2933625 0.54163
## scale(outdegree) 0.0256675 0.16021 -0.38
## scale(indegree) 0.0002987 0.01728 -0.55 -0.56
## Residual 0.6454951 0.80343
## Number of obs: 1985, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.726e-01 7.529e-02 2.591e+02 2.292 0.02270 *
## scale(outdegree) 8.732e-02 2.912e-02 8.263e+01 2.999 0.00358 **
## scale(indegree) 3.735e-02 2.118e-02 6.440e+02 1.763 0.07837 .
## numID -9.835e-03 4.891e-03 1.879e+02 -2.011 0.04577 *
## scale(length) -8.188e-04 2.192e-02 1.945e+03 -0.037 0.97020
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(t) scl(n) numID
## scale(tdgr) 0.189
## scale(ndgr) 0.117 -0.125
## numID -0.806 -0.264 -0.151
## scal(lngth) -0.087 -0.081 0.142 0.056
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m<-lmer( scale(Dist) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(Dist) ~ scale(strengthOut) + scale(strengthIn) + numID +
## scale(length) + (scale(strengthOut) + scale(strengthIn) | subID)
## Data: fullData
##
## REML criterion at convergence: 5121.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8859 -0.5442 0.1147 0.6225 2.6721
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.291166 0.53960
## scale(strengthOut) 0.019970 0.14132 -0.53
## scale(strengthIn) 0.003752 0.06126 0.02 0.22
## Residual 0.641282 0.80080
## Number of obs: 1985, groups: subID, 209
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.969e-01 7.513e-02 2.660e+02 2.621 0.00927 **
## scale(strengthOut) 1.358e-01 2.827e-02 5.135e+01 4.804 1.39e-05 ***
## scale(strengthIn) 1.568e-02 2.575e-02 1.906e+01 0.609 0.54975
## numID -1.147e-02 4.844e-03 1.927e+02 -2.367 0.01894 *
## scale(length) -6.898e-04 2.178e-02 1.930e+03 -0.032 0.97474
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(O) scl(I) numID
## scl(strngO) 0.207
## scl(strngI) 0.120 -0.078
## numID -0.805 -0.319 -0.085
## scal(lngth) -0.079 -0.063 0.139 0.050Do people higher in self-esteem have more self than other focused memories?
fullData$SminO <- fullData$SO_1 - fullData$SO_2
m<-lmer( SminO ~ SE + ( 1 | subID), data=fullData)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SminO ~ SE + (1 | subID)
## Data: fullData
##
## REML criterion at convergence: 17813.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8301 -0.5846 -0.1584 0.6071 2.8011
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 287 16.94
## Residual 1191 34.50
## Number of obs: 1776, groups: subID, 206
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 20.1316 6.8611 197.7275 2.934 0.00374 **
## SE 0.4568 2.9956 200.2941 0.152 0.87896
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## SE -0.974Page Rank, Hub and Changeability
m<-lmer(Chan ~ page + ( page | subID), data=fullData)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00201605 (tol = 0.002, component 1)summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Chan ~ page + (page | subID)
## Data: fullData
##
## REML criterion at convergence: 7451.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5506 -0.5188 0.1435 0.6380 2.4692
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.7239 0.8508
## page 2.8202 1.6794 -0.37
## Residual 1.8739 1.3689
## Number of obs: 2067, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.28397 0.09343 139.75154 56.555 <2e-16 ***
## page 0.53139 0.39896 155.86021 1.332 0.185
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## page -0.686
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00201605 (tol = 0.002, component 1)m<-lmer(Chan ~ pageW + ( pageW | subID), data=fullData)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Chan ~ pageW + (pageW | subID)
## Data: fullData
##
## REML criterion at convergence: 7450
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5729 -0.5228 0.1492 0.6423 2.4633
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.7045 0.8393
## pageW 2.1633 1.4708 -0.31
## Residual 1.8739 1.3689
## Number of obs: 2067, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.25817 0.09159 146.51342 57.41 <2e-16 ***
## pageW 0.68403 0.38436 163.86429 1.78 0.077 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## pageW -0.669m<-lmer(Chan ~ pageOut + ( pageOut | subID), data=fullData)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Chan ~ pageOut + (pageOut | subID)
## Data: fullData
##
## REML criterion at convergence: 7423
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5889 -0.5275 0.1288 0.6339 2.2266
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.9007 0.949
## pageOut 5.2869 2.299 -0.70
## Residual 1.8388 1.356
## Number of obs: 2067, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.18192 0.09275 168.21829 55.872 < 2e-16 ***
## pageOut 1.32770 0.34736 165.28688 3.822 0.000187 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## pageOut -0.705m<-lmer(Chan ~ pageOutW + ( pageOutW | subID), data=fullData)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Chan ~ pageOutW + (pageOutW | subID)
## Data: fullData
##
## REML criterion at convergence: 7424.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5684 -0.5271 0.1284 0.6323 2.2400
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.8643 0.9297
## pageOutW 4.8983 2.2132 -0.65
## Residual 1.8400 1.3565
## Number of obs: 2067, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.16388 0.09199 168.14558 56.134 < 2e-16 ***
## pageOutW 1.40907 0.35061 164.58161 4.019 8.88e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## pageOutW -0.692m<-lmer(Chan ~ hub + ( hub | subID), data=fullData)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Chan ~ hub + (hub | subID)
## Data: fullData
##
## REML criterion at convergence: 7374.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6191 -0.5154 0.1350 0.6102 2.6256
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.8484 0.9211
## hub 0.6631 0.8143 -0.56
## Residual 1.7554 1.3249
## Number of obs: 2067, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.14426 0.08313 180.94385 61.881 < 2e-16 ***
## hub 0.63695 0.10737 199.75811 5.932 1.3e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## hub -0.601m<-lmer(Chan ~ hubW + ( hubW | subID), data=fullData)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Chan ~ hubW + (hubW | subID)
## Data: fullData
##
## REML criterion at convergence: 7370.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6127 -0.5366 0.1385 0.6163 2.6130
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.8089 0.8994
## hubW 0.5306 0.7284 -0.53
## Residual 1.7619 1.3274
## Number of obs: 2067, groups: subID, 211
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.15300 0.07975 188.71394 64.614 < 2e-16 ***
## hubW 0.69360 0.10529 232.85966 6.588 2.95e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## hubW -0.548fullShort <- do.call(data.frame, # Replace Inf in data by NA
lapply(fullShort,
function(x) replace(x, is.infinite(x), NA)))
corMat <- fullShort %>% select(edgeTot:NFC) %>% cor(fullShort,use="pairwise.complete.obs")
outphm <- pheatmap(corMat, fontsize_row = 6, fontsize_col = 6, angle_col = 45, angle_row =45, width=100, height = 200 )
heatmaply_cor(round(corMat,3), Rowv=outphm[[1]], Colv=outphm[[2]], revC=TRUE, fontsize_row = 2.5, fontsize_col = 2.5, angle_col = 45, angle_row =45, limits = c(-1, 1), colors = colorRampPalette(rev(brewer.pal(n = 7, name =
"RdYlBu")))(100) )fullShort %>% select(vad_compAg, MAIA:NFC) %>% corToOne(., "vad_compAg")## Loading required package: corrr##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(referenceVar)` instead of `referenceVar` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.fullShort %>% select(vad_compAg, MAIA:NFC) %>% plotCorToOne(., "vad_compAg")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(edgeTot, MAIA:NFC) %>% corToOne(., "edgeTot")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'fullShort %>% select(edgeTot, MAIA:NFC) %>% plotCorToOne(., "edgeTot")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(numID, MAIA:NFC) %>% corToOne(., "numID")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'fullShort %>% select(numID, MAIA:NFC) %>% plotCorToOne(., "numID")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(dense, MAIA:NFC) %>% corToOne(., "dense")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'fullShort %>% select(dense, MAIA:NFC) %>% plotCorToOne(., "dense")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(aveDist, MAIA:NFC) %>% corToOne(., "aveDist")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'fullShort %>% select(aveDist, MAIA:NFC) %>% plotCorToOne(., "aveDist")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Val_1_Homoph, MAIA:NFC) %>% corToOne(., "Val_1_Homoph")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'fullShort %>% select(Val_1_Homoph, MAIA:NFC) %>% plotCorToOne(., "Val_1_Homoph")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Val_2_Homoph, MAIA:NFC) %>% corToOne(., "Val_2_Homoph")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'fullShort %>% select(Val_2_Homoph, MAIA:NFC) %>% plotCorToOne(., "Val_2_Homoph")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Fund_Homoph, MAIA:NFC) %>% corToOne(., "Fund_Homoph")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'fullShort %>% select(Fund_Homoph, MAIA:NFC) %>% plotCorToOne(., "Fund_Homoph")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Rep_Homoph, MAIA:NFC) %>% corToOne(., "Rep_Homoph")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'fullShort %>% select(Rep_Homoph, MAIA:NFC) %>% plotCorToOne(., "Rep_Homoph")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Chan_Homoph, MAIA:NFC) %>% corToOne(., "Chan_Homoph")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'fullShort %>% select(Chan_Homoph, MAIA:NFC) %>% plotCorToOne(., "Chan_Homoph")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(recip, MAIA:NFC) %>% corToOne(., "recip")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'fullShort %>% select(recip, MAIA:NFC) %>% plotCorToOne(., "recip")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
Cohesive
fullShort %>% select(cohes, MAIA:NFC) %>% corToOne(., "cohes")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'fullShort %>% select(cohes, MAIA:NFC) %>% plotCorToOne(., "cohes")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(modular, MAIA:NFC) %>% corToOne(., "modular")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'fullShort %>% select(modular, MAIA:NFC) %>% plotCorToOne(., "modular")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
Reciprocity
fullShort %>% select(recip, MAIA:NFC) %>% corToOne(., "recip")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'fullShort %>% select(recip, MAIA:NFC) %>% plotCorToOne(., "recip")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
Standard Deviation of Degrees
fullShort %>% select(sdDeg, MAIA:NFC) %>% corToOne(., "sdDeg")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'fullShort %>% select(sdDeg, MAIA:NFC) %>% plotCorToOne(., "sdDeg")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(sdDegW, MAIA:NFC) %>% corToOne(., "sdDegW")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'fullShort %>% select(sdDegW, MAIA:NFC) %>% plotCorToOne(., "sdDegW")## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'